arXiv subjects

J. Marro

Publications and source records attributed to J. Marro.

At least 37 records · Page 2Linked to original sources

arXiv2006

Competition between synaptic depression and facilitation in attractor neural networks

We study the effect of competition between short-term synaptic depression and facilitation on the dynamical properties of attractor neural networks, using Monte Carlo simulation and a mean field analysis. Depending on the balance between depression, facilitation and the noise, the network displays different behaviours, including associative memory and switching of the activity between different attractors. We conclude that synaptic facilitation enhances the attractor instability in a way that (i) intensifies the system adaptability to external stimuli, which is in agreement with experiments, and (ii) favours the retrieval of information with less error during short--time intervals.

q-bio.NC↗

arXiv2006

Chaotic hopping between attractors in neural networks

We present a neurobiologically--inspired stochastic cellular automaton whose state jumps with time between the attractors corresponding to a series of stored patterns. The jumping varies from regular to chaotic as the model parameters are modified. The resulting irregular behavior, which mimics the state of attention in which a systems shows a great adaptability to changing stimulus, is a consequence in the model of short--time presynaptic noise which induces synaptic depression. We discuss results from both a mean--field analysis and Monte Carlo simulations.

q-bio.NC↗

arXiv2006

Instability of attractors in autoassociative networks with bioinspired fast synaptic noise

We studied autoassociative networks in which synapses are noisy on a time scale much shorter that the one for the neuron dynamics. In our model a presynaptic noise causes postsynaptic depression as recently observed in neurobiological systems. This results in a nonequilibrium condition in which the network sensitivity to an external stimulus is enhanced. In particular, the fixed points are qualitatively modified, and the system may easily scape from the attractors. As a result, in addition to pattern recognition, the model is useful for class identification and categorization.

q-bio.NC↗

arXiv2006

Algorithms for identification and categorization

The main features of a family of efficient algorithms for recognition and classification of complex patterns are briefly reviewed. They are inspired in the observation that fast synaptic noise is essential for some of the processing of information in the brain.

q-bio.NC↗

arXiv2006

Non-equilibrium Anisotropic Phases, Nucleation and Critical Behavior in a Driven Lennard-Jones Fluid

We describe short-time kinetic and steady-state properties of the non--equilibrium phases, namely, solid, liquid and gas anisotropic phases in a driven Lennard-Jones fluid. This is a computationally-convenient two-dimensional model which exhibits a net current and striped structures at low temperature, thus resembling many situations in nature. We here focus on both critical behavior and details of the nucleation process. In spite of the anisotropy of the late--time spinodal decomposition process, earlier nucleation seems to proceed by Smoluchowski coagulation and Ostwald ripening, which are known to account for nucleation in equilibrium, isotropic lattice systems and actual fluids. On the other hand, a detailed analysis of the system critical behavior rises some intriguing questions on the role of symmetries; this concerns the computer and field-theoretical modeling of non-equilibrium fluids.

cond-mat.soft↗

arXiv2006

Understanding Scale Invariance in a Minimal Model of Complex Relaxation Phenomena

We report on the computer study of a lattice system that relaxes from a metastable state. Under appropriate nonequilibrium randomness, relaxation occurs by avalanches, i.e., the model evolution is discontinuous and displays many scales in a way that closely resembles the relaxation in a large number of complex systems in nature. Such apparent scale invariance simply results in the model from summing over many exponential relaxations, each with a scale which is determined by the curvature of the domain wall at which the avalanche originates. The claim that scale invariance in a nonequilibrium setting is to be associated with criticality is therefore not supported. Some hints that may help in checking this experimentally are discussed.

cond-mat.dis-nn↗

arXiv2005

Lennard--Jones and Lattice Models of Driven Fluids

We introduce a nonequilibrium off--lattice model for anisotropic phenomena in fluids. This is a Lennard--Jones generalization of the driven lattice--gas model in which the particles' spatial coordinates vary continuously. A comparison between the two models allows us to discuss some exceptional, hardly realistic features of the original discrete system --which has been considered a prototype for nonequilibrium anisotropic phase transitions. We thus help to clarify open issues, and discuss on the implications of our observations for future investigation of anisotropic phase transitions.

cond-mat.stat-mech↗

arXiv2005

Switching between memories in neural automata with synaptic noise

We present a stochastic neural automata in which activity fluctuations and synaptic intensities evolve at different temperature, the latter moving through a set of stored patterns. The network thus exhibits various retrieval phases, including one which depicts continuous switching between attractors. The switching may be either random or more complex, depending on the system parameters values.

cond-mat.stat-mech↗

arXiv2005

Control of neural chaos by synaptic noise

We studied neural automata -or neurobiologically inspired cellular automata- which exhibits chaotic itinerancy among the different stored patterns or memories. This is a consequence of activity-dependent synaptic fluctuations, which continuously destabilize the attractor and induce irregular hopping to other possible attractors. The nature of the resulting irregularity depends on the dynamic details, namely, on the intensity of the synaptic noise and on the number of sites of the network that are synchronously updated at each time step. Varying these details, different regimes occur from regular to chaotic. In the absence of external agents, the chaotic behavior may turn regular after tuning the noise intensity. It is argued that a similar mechanism might be at the origin of the self-control of chaos in natural systems.

q-bio.NC↗

arXiv2005

Effects of fast presynaptic noise in attractor neural networks

We study both analytically and numerically the effect of presynaptic noise on the transmission of information in attractor neural networks. The noise occurs on a very short-time scale compared to that for the neuron dynamics and it produces short-time synaptic depression. This is inspired in recent neurobiological findings that show that synaptic strength may either increase or decrease on a short-time scale depending on presynaptic activity. We thus describe a mechanism by which fast presynaptic noise enhances the neural network sensitivity to an external stimulus. The reason for this is that, in general, the presynaptic noise induces nonequilibrium behavior and, consequently, the space of fixed points is qualitatively modified in such a way that the system can easily scape from the attractor. As a result, the model shows, in addition to pattern recognition, class identification and categorization, which may be relevant to the understanding of some of the brain complex tasks.

q-bio.NC↗

arXiv2004

Theoretical Characterization of the Interface in a Nonequilibrium Lattice System

The influence of nonequilibrium bulk conditions on the properties of the interfaces exhibited by a kinetic Ising--like model system with nonequilibrium steady states is studied. The system is maintained out of equilibrium by perturbing the familiar spin--flip dynamics at temperature T with completely--random flips; one may interpret these as ideally simulating some (dynamic) impurities. We find evidence that, in the present case, the nonequilibrium mechanism adds to the basic thermal one resulting on a renormalization of microscopic parameters such as the probability of interfacial broken bonds. On this assumption, we develop theory for the nonequilibrium "surface tension", which happens to show a non--monotonous behavior with a maximum at some finite T. It ensues, in full agreement with Monte Carlo simulations, that interface fluctuations differ qualitatively from the equilibrium case, e.g., the interface remains rough at zero--T. We discuss on some consequences of these facts for nucleation theory, and make some explicit predictions concerning the nonequilibrium droplet structure.

cond-mat.stat-mech↗

arXiv2004

Reentrant Behavior of the Spinodal Curve in a Nonequilibrium Ferromagnet

The metastable behavior of a kinetic Ising--like ferromagnetic model system in which a generic type of microscopic disorder induces nonequilibrium steady states is studied by computer simulation and a mean--field approach. We pay attention, in particular, to the spinodal curve or intrinsic coercive field that separates the metastable region from the unstable one. We find that, under strong nonequilibrium conditions, this exhibits reentrant behavior as a function of temperature. That is, metastability does not happen in this regime for both low and high temperatures, but instead emerges for intermediate temperature, as a consequence of the non-linear interplay between thermal and nonequilibrium fluctuations. We argue that this behavior, which is in contrast with equilibrium phenomenology and could occur in actual impure specimens, might be related to the presence of an effective multiplicative noise in the system.

cond-mat.stat-mech↗

arXiv2003

Effects of Static and Dynamic Disorder on the Performance of Neural Automata

We report on both analytical and numerical results concerning stochastic Hopfield--like neural automata exhibiting the following (biologically inspired) features: (1) Neurons and synapses evolve in time as in contact with respective baths at different temperatures. (2) The connectivity between neurons may be tuned from full connection to high random dilution or to the case of networks with the small--world property and/or scale-free architecture. (3) There is synaptic kinetics simulating repeated scanning of the stored patterns. Though these features may apparently result in additional disorder, the model exhibits, for a wide range of parameter values, an extraordinary computational performance, and some of the qualitative behaviors observed in natural systems. In particular, we illustrate here very efficient and robust associative memory, and jumping between pattern attractors.

cond-mat.stat-mech↗

arXiv2003

Influence of topology on the performance of a neural network

We studied the computational properties of an attractor neural network (ANN) with different network topologies. Though fully connected neural networks exhibit, in general, a good performance, they are biologically unrealistic, as it is unlikely that natural evolution leads to such a large connectivity. We demonstrate that, at finite temperature, the capacity to store and retrieve binary patterns is higher for ANN with scale--free (SF) topology than for highly random--diluted Hopfield networks with the same number of synapses. We also show that, at zero temperature, the relative performance of the SF network increases with increasing values of the distribution power-law exponent. Some consequences and possible applications of our findings are discussed.

cond-mat.stat-mech↗

arXiv2003

Metastability and Avalanches in a Nonequilibrium Ferromagnetic System

We present preliminary results on the metastable behavior of a nonequilibrium ferromagnetic system. The metastable state mean lifetime is a non-monotonous function of temperature; it shows a maximum at certain non-zero temperature which depends on the strengh of the nonequilibrium perturbation. This is in contrast with the equilibrium case in which lifetime increases monotonously as the temperature is decreasesed. We also report on avalanches during the decay from the metastable state. Assuming both free boundaries and nonequilibrium impurities, the avalanches exhibit power-law size and lifetime distributions. Such scale free behavior is very sensible. The chances are that our observations may be observable in real (i.e. impure) ferromagnetic nanoparticles.

cond-mat.stat-mech↗

arXiv2003

Kinetics of Phase Separation in the Driven Lattice Gas: Self-Similar Pattern Growth under Anisotropic Nonequilibrium Conditions

The driven lattice gas (DLG) evolving at low temperature helps understanding the kinetics of pattern formation in unstable mixtures under anisotropic conditions. We here develop a simple theoretical description of kinetics in Monte Carlo simulations of the DLG. A Langevin continuum analog is also studied which is shown to exhibit the same behavior. We demonstrate that pattern growth is mainly a consequence of single-particle processes and that, after a short transient time, in which a surface evaporation/condensation mechanism is important, hole diffusion in the bulk becomes dominant. Consequently, there is a unique relevant length that behaves $l(t) \sim t^{1/3}$ for macroscopic systems except at some very early (perhaps unobservable) time. This implies sort of self-similarity, namely, the spatial pattern looks alike, but for a (non-trivial) change of scale at different times. We also characterize the structure factor, in which we identify Guinier and Porod regions, and its scaling form with both time and size. The underlying anisotropy turns out to be essential in determining the macroscopically-emergent peculiar behavior.

cond-mat.stat-mech↗

arXiv2002

Modeling Nonequilibrium Phase Transitions and Critical Behavior in Complex Systems

We comment on some recent, yet unpublished results concerning instabilities in complex systems and their applications. In particular, we briefly describe main observations during extensive computer simulations of two lattice nonequilibrium models. One exhibits robust and efficient processes of pattern recognition under synaptic coherent activity; the second example exhibits interesting critical behavior and simulates nucleation and spinodal decomposition processes in driven fluids.

cond-mat.stat-mech↗

arXiv2002

Growth and Scaling in Anisotropic Spinodal Decomposition

We studied phase separation in a particle interacting system under a large drive along x. We here identify the basic growth mechanisms, and demonstrate time self-similarity, finite-size scaling, as well as other interesting features of both the structure factor and the scaling function. We also show that, at late t in two dimensions, there is a unique t-dependent length increasing l_y(t) \sim t^{1/3} for macroscopic systems. Our results, which follow as a direct consequence of the underlying anisotropy, may characterize a class of nonequilibrium situations.

cond-mat.stat-mech↗